Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
The office action sent in response to Applicant’s communication received on 9/27/2024 for the application number 18776711. The office hereby acknowledges receipt of the following placed of record in the file: Specification, Abstract, Oath/Declaration and claims.
Priority
This application claims priority from Chinese application CN202310906808.5, filed July 21, 2023,
Status of the claims
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/18/2024 filed before the mailing date of first office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 101.
Claim 14 includes:
A computing device, comprising a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, the computing device is caused to: (a) read graph data and ontology information of a subgraph in the knowledge graph, wherein the graph data comprises several triplets comprising graph elements in the subgraph, and the ontology information comprises at least a type of each graph element in the subgraph; (b) generate several sentences based on several pre-constructed sentence templates, the graph data, and the ontology information, wherein the several sentences are classified into a generated sentence set, and at least one of the several sentence templates is constructed based on the ontology information; and (c) determine a text corpus corresponding to the subgraph based on the generated sentence set, (d) wherein the text corpus is used to train a language model
Steps (a)-(c) can be performed by human mind as given an knowledge subgraph graph and ontology information, human can construct sentences based on triples information.
Step (d) recites a post solutional activity
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least a computing device, hence a machine. Thus, the claim is , recites a statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. As discussed above, the broadest reasonable interpretation of steps (a)-(c) that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. Step (a) recites reading a graph and its ontology which human can do, step b recites generating a plural sentence given a graph and the ontology information. A human can determine the triplets from the graph and create many sentences. A human can relates a specific text with its subgraphs. Hence, these steps can be performed by a human, using “observation, evaluation, judgment, [and] opinion,” because they involve making determinations and identifications, which are mental tasks humans routinely do,' ” and thus can practically be performed in the human mind, In re Killian, 45 F.4th 1373, 1379 (Fed. Cir. 2022). Therefore, these limitations are considered together as a abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). Claim requires A computing device, comprising a memory and a processor, and (e) wherein the text corpus is used to train a language model. Step (e) training a model is an insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). The limitations of “processor”, “memory” and non-transitory computer readable medium provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. At Step 2A, Prong Two, the second additional element of memory and processor and computer readable medium was found to represent no more than mere instructions to apply the judicial exception on a computer using generic computer components. The analysis under Step 2A, Prong Two is carried through to Step 2B. Further, the first additional element in step (d) was found to be insignificant extra-solution activity. However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the re-evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). Here, the step of training that is recited at a high level of generality, and as discussed in the disclosure, is well-understood ( background- learning a large quality of samples) ). Therefore, this limitation remains insignificant extra solution activity even upon reconsideration and does not amount to significantly more. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, and therefore do not provide an inventive concept (Step 2B: NO). The claim is not eligible.
Regarding claim 1 and 13, analysis applicable to claim 14, are applicable.
Regarding claims 2-12 and 15-20, they recite additional mental steps to create sentences and hence similar analysis analogous to claim 14, are applicable.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-10 and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Oltramari ( US 20220147861) and in view of Hoang (US 20230134798)
Regarding claim 1, Oltramari teaches a method for generating a text by using a knowledge graph ( candidate responses, Para 0046, Fig 3-4) , wherein graph elements of the knowledge graph comprise a node representing an entity and a connecting edge representing a relationship between nodes ( the data structure is a triple, which includes a head element, a relationship element, and a tail element, which may be denoted as (h, r, t), whereby ‘h’ represents the head element (or subject), ‘r’ represents the relationship element (or a relation), and ‘t’ represents the tail element (or an object), Para 0029), and the method comprises: reading graph data and ontology information of a subgraph in the knowledge graph (obtain global knowledge graph, Para 0028) , wherein the graph data comprises several triplets comprising graph elements in the subgraph ( step 316 – extract pool of triples, Fig 4, 220b, Para 0036) , and the ontology information comprises at least a type of each graph element in the subgraph ( Each triple includes a head element, a relationship element, and a tail element, as expressed by (h′, r′, t′), where “h′” represents the head element (or a subject), “r′” represents the relationship element (or a relation), and “t′” represents the tail element (or an object), Para 0036) ; generating several sentences based on several pre-constructed sentence templates, the graph data, and the ontology information ( pool object of distractor candidates, step 320, Para 0038-0039) , wherein the several sentences are classified ( a,b,c, Fig 4) , and at least one of the several sentence templates is constructed based on the ontology information ( for e.g. relationship “used in”…, Para ..38-0039); and determining a text corpus corresponding to the subgraph based on the generated sentence set ( response options based on graphs, Fig 4, Para 0038-0042) , wherein the text is used to train a language model ( training the model, step 324, Para 0048)
Oltramari does not explicitly teaches text corpus from knowledge graph
In the same field of endeavor Hoang teaches text corpus from knowledge graph and wherein the several sentences are classified into a generated sentence set ( The training data may be created by: 1) creating a set of keywords from the list of triples and 2) searching a text corpus using an information retrieval system to find top relevant candidate fragment of texts that match the keywords created from the input triples. These fragment of texts are considered as the candidate labels for the given list of triples., Para 0068)
It would have been obvious having the teachings of Oltramari to further have the concept of Oshin before effective filing date since the approach converts the KG into natural text, allowing it to be seamlessly integrated into existing language models. It carries the further advantages of improved factual accuracy and reduced toxicity in the resulting language model ( Para 0026, Hoang)
Regarding claim 2, Oltramari as above in claim 1, teaches wherein any triplet in the several triplets comprises a head node, a connecting edge, and a tail node, and the step of generating several sentences comprises: generating several sentences corresponding to the any triplet based on the several pre-constructed sentence templates ( In this case, the data structure is a triple, which includes a head element, a relationship element, and a tail element, which may be denoted as (h, r, t), whereby ‘h’ represents the head element (or subject), ‘r’ represents the relationship element (or a relation), and ‘t’ represents the tail element (or an object)., Para 0029; template = lexicalization, Para 0031)
Regarding claim 3, Oltramari as above in claim 2, teaches, wherein the several sentence templates comprise a first-type template, the several sentences comprise a first sentence, and for the first sentence, a name of the head node is used as a subject, a relationship type corresponding to the connecting edge is used as a verb, and a name of the tail node is used as an object ( Para 0029-0031)
Regarding claim 4, Oltramari as above in claim 2, teaches, wherein the several sentence templates comprise a second-type template, the several sentences comprise a second sentence, and for the second sentence, a type of the head node is used as a subject, a relationship type corresponding to the connecting edge is used as a verb, and a type of the tail node is used as an object ( Para 0029-0031, Fig 3)
Regarding claim 5, Oltramari as above in claim 1, teaches, wherein the step of generating several sentences comprises: extracting node information of a target node from the graph data and the ontology information, wherein the node information comprises a node name and a node type ( extracting a pool of data structures from the global knowledge graph based on a set of distractor criteria., Para 0005; data structure comprises head node and tail node, Para 0028) ; and generating several sentences corresponding to the target node based on the several pre-constructed sentence templates and the node information ( generate responses, Fig 3b, 4)
Regarding clam 6, Oltramari as above in claim 5, teaches wherein the several sentence templates comprise a third-type template, the several sentences comprise a third sentence, and for the third sentence, the node type is used as a subject, a preset word representing an inclusive relationship is used as a verb, and the node name is used as an object ( Each triple includes a head element, a relationship element, and a tail element, as expressed by (h′, r′, t′), where “h′” represents the head element (or a subject), “r′” represents the relationship element (or a relation), and “t′” represents the tail element (or an object)., Para 0036-0037; relationship element in knowledge graph is considered verb)
Regarding claim 7, Oltramari as above in claim 1, teaches obtaining several logical reasoning rules determined from the knowledge graph, wherein the logical reasoning rule comprises the ontology information of the knowledge graph ( distractor criteria, Para 0036) ; matching the graph data and the ontology information with each of the several logical reasoning rules, to obtain a matching rule ( triple associated with distractor criteria, Para 0036) ; and combining the graph data with the matching rule, to generate a corresponding sentence ( The set of response options include the correct answer and the set of distractors., Para 0006, Claim 1) , wherein the sentence is classified into the generated sentence set ( label the sentences, Para 0068, 0079 Hoang)
Regarding claim 8, Oltramari modified by Hoang as above in claim 1, teaches , wherein any logical reasoning rule comprises a logical condition and a reasoning result; the step of matching the graph data and the ontology information with each of the several logical reasoning rules comprises: matching the graph data and the ontology information with a logical condition of each of the several logical reasoning rules; and the step of generating a corresponding sentence comprises: combining node information in the graph data with a reasoning result of the matching rule ( fig 3b, Oltramari)
Regarding claim 9, Oltramari s above in claim 7, teaches wherein a confidence of the matching rule is a first confidence; and the step of generating a corresponding sentence comprises: determining a first probability descriptor corresponding to the first confidence from a preset correspondence between a confidence and a probability descriptor; and combining the graph data with the matching rule, and adding the first probability descriptor to the generated sentence ( based on the score determine the distractor element, Para 0044)
Regarding claim 10, Oltramari modified by Hoang as above in claim 1, teaches wherein the step of determining a text corpus corresponding to the subgraph comprises: combining a plurality of sentences in the generated sentence set, and using a sentence obtained through combination as the text corpus corresponding to the subgraph ( Fig 3b, Oltramari; training dataset, Para 0068, Hoang)
Regarding claim 13, arguments analogous to claim 1, are applicable. In addition, Oltramari teaches A non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores a computer program, which when executed by a processor causes the processor to perform a method of claim 1 ( Para 0005)
Regarding claim 14, arguments analogous to claim 1, are applicable. In addition, Oltramari teaches A computing device, comprising a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, the computing device is caused to perform the method of claim 1 ( system, abtract)
Regarding claim 15, arguments analogous to claim 2, are applicable.
Regarding claim 16, arguments analogous to claim 3, are applicable.
Regarding claim 17, arguments analogous to claim 4, are applicable.
Regarding claim 18, arguments analogous to claim 5, are applicable.
Regarding claim 19, arguments analogous to claim 6, are applicable.
Regarding claim 20, arguments analogous to claim 7, are applicable.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Oltramari ( US 20220147861) and further in view of Hoang (US 20230134798) and further in view of Ramnani (US 20230071799)
Regarding claim 11, Oltramari modified by Huang teaches and combining the duplicate-free generated sentence set( corpus, Abstract, Hoang training data, Fig 3, Oltramari) however does not explicitly teach wherein the step of combining a plurality of sentences in the generated sentence set comprises: deduplicating the plurality of sentences in the generated sentence set
Ramnani teaches deduplicating the plurality of sentences in the generated sentence set ( removing any duplicate sentences that have a similarity with a given sentence above a certain threshold, Para 0065)
It would have been obvious having the teachings of Oltramari and Hoang to further include the concept of Ramnani before effective filing date so to have a concise text
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Oltramari ( US 20220147861) in view of Hoang (US 20230134798) and further in view of Ebata (US 20220171927)
Regarding claim 12, Oltramari modified by Hoang as above in claim 10, does not explicitly teach wherein the step of combining a plurality of sentences in the generated sentence set comprises: selecting to-be-combined sentences from the generated sentence set, and combining the to-be-combined sentences, wherein the to-be-combined sentences comprise sentences with the same subject and verb and sentences with the same verb and object
However, Ebata teaches the step of combining a plurality of sentences in the generated sentence set comprises: selecting to-be-combined sentences from the generated sentence set, and combining the to-be-combined sentences, wherein the to-be-combined sentences comprise sentences with the same subject and verb and sentences with the same verb and object ( an example in which multiple sentences are assessed as being combinable if the sentences have the same subject was described. However, the invention is not limited to this example. For example, sentences may be assessed as being combinable if the objects or predicates are the same. Additionally, sentences may be assessed as being combinable if the subjects and modifiers of the subjects are the same. Additionally, sentences may be assessed as being combinable if two or more combinations of the subject, the predicate and the object, are the same. Whether or not multiple sentences can be combined depends on natural language combination rules (for example, general natural language grammar) that are independent of formal rule sets., Para 0054)
It would have been obvious having the teachings of Oltramari and Hoang to further include the concept of Ebata before effective filing date to have concise set of data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Daniel(Generating Natural Language from Linked Data: Unsupervised template extraction) discloses generating natural language from Linked Data that automatically learns sentence templates and statistical document planning from parallel RDF datasets and text. We have built a proof-of-concept system (LOD-DEF) trained on un-annotated text from the Simple English Wikipedia and RDF triples from DBpedia, focusing exclusively on factual, non-temporal information. The goal of the system is to generate short descriptions, equivalent to Wikipedia stubs, of entities found in Linked Datasets
Oshin (Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training) discloses generating text from KG. First, the entity subgraphs are created and then converted to the sentence ( fig 1, 2)
Benjamin ( Claim Extraction via Subgraph Matching over Modal and Syntactic Dependencies )
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/Richa Sonifrank/Primary Examiner, Art Unit 2654